Data Analysis, Data Science, Data Understanding

Course Notes for uOttawa’s Professional Data Science Certificate

Patrick Boily, Chunyun Ma

2021-12-16

Welcome

This website contains draft chapters for Data Analysis, Data Science, Data Understanding, the course notes used for uOttawa’s Professional Data Science Certificate.

Colophone

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This online version of the book was built with:

# xfun::session_info(c(cran_packages, gh_repos_bare), dependencies = FALSE)

License

This work by Patrick Boily and Chunyun Ma is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Contributors

A reference manual of this size could not have been compiled without the help of a multitude of individuals over the years:

• Oliver Benning contributed to Reinforcement Learning;

• Kevin Cheung contributed to A Survey of Optimization, An Introduction to Deep Learning, and Data Science with Streams;

• Youssouph Cissokho contributed to Anomaly Detection and Outlier Analysis;

• Stephen Davies influenced Data Visualization;

• Soufiane Fadel contributed to Anomaly Detection and Outlier Analysis and Reinforcement Learning;

• Patrick Farrell influenced Survey Sampling Methods;

• Ehssan Ghashim contributed to Programming Fundamentals, Data Visualization, and Bayesian Data Analysis;

• Shintaro Hagiwara contributed to Introductory Statistical Analysis;

• Lani Haque contributed to Web Scraping and Automated Data Collection and Network Data Analysis;

• Rafal Kulik influenced Overview of Probability Theory, Introductory Statistical Analysis, A Linear Regression Cheatsheet, and A Primer of Times Series and Forecasting;

• Gilles Lamothe influenced Overview of Probability Theory, Introductory Statistical Analysis, and A Linear Regression Cheatsheet;

• Oliver Leduc contributed to Reression and Value Estimation, Spotlight on Clustering, and Feature Selection and Dimension Reduction;

• Dong (Elle) Liu contributed to A Primer of Times Series and Forecasting;

• Andrew Macfie contributed to Web Scraping and Automated Data Collection, Feature Selection and Dimension Reduction, Natural Language Processing, and Big Data and Parallel Computing;

• Aditya Maheshwari contributed to Spotlight on Clustering and Feature Selection and Dimension Reduction;

• Richard Millson contributed to Anomaly Detection and Outlier Analysis and Network Data Analysis;

• Rachel Ostic contributed to Reinforcement Learning;

• Kate Park contributed to Network Data Analysis;

• Smit Patel contributed to An Introduction to Deep Learning;

• Maia Pelletier contributed to Data Visualization and Feature Selection and Dimension Reduction;

• Razieh Pourhasan contributed to Anomaly Detection and Outlier Analysis and An Introduction to Deep Learning;

• Bronwyn Rayfield contributed to Spotlight on Classification;

• Mohsen Rezapour Tougari contributed to Recommender Engines;

• Jen Schellinck contributed to Programming Fundamentals, Introduction to Simulations, Non-Technical Aspects of Quantitative Work, Data Science Basics, Machine Learning 101, Spotlight on Clustering;

• Bing Wang contributed to Data Science with Streams.

A hearty “thank you” to everyone, and to all others who have crossed our paths on this data adventure!